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All Journal International Journal of Computer and Information System (IJCIS) Journal La Multiapp Malcom: Indonesian Journal of Machine Learning and Computer Science
Shodikin, Mohamad
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Evaluating Effective Social Media Marketing With Artificial Intelligence Using The AIDA Model Approach Alia, Putri Ariatna; Cahyono, Warna Agung; Shodikin, Mohamad; Meisyarani, Jihan Salsabila; Sani, Rosi Rijal; Kriswibowo, Rony
International Journal of Computer and Information System (IJCIS) Vol 5, No 4 (2024): IJCIS : Vol 5 - Issue 4 - 2024
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v5i4.205

Abstract

The rapid growth of information and communication technology, especially social media, has significantly changed the marketing landscape. In the face of this challenge, companies are increasingly adopting marketing strategies through social media to reach their intended target markets. This research aims to analyze the effectiveness of Social Media Marketing by utilizing Artificial Intelligence, especially ChattGPT and applying the AIDA Model approach (Awareness, Interest, Desire, Action). This research methodology uses descriptive qualitative methods with data collection through virtual ethnography and interviews from the Babelubozz online store using the Instagram platform: @grosirhijabtermurahsidoarjo. Artificial intelligence algorithms to analyze interaction patterns and user responses. The AIDA (Awareness, Interest, Desire, Action) model. used as a basis for measuring the stages of consumer awareness, interest, desire, and action in the context of marketing through social media. The results of this analysis provide an in-depth understanding of how companies can improve the effectiveness of their marketing campaigns on social media by optimizing each stage of the AIDA Model. Therefore, the marketing and promotional content generated by ChatGPT is able to increase the effectiveness of social media marketing.
Security Quality Measurement Based on ISO/IEC 25023 Quality Model Case Study: Hospital Management Information System Taniasari, Nungky; Shodikin, Mohamad
Journal La Multiapp Vol. 5 No. 5 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i5.1626

Abstract

Hospitals need to protect the security of data assets, where data assets are an important part of the continuity of hospital operations. In connection with the protection of data and information assets in the hospital, it has become a requirement for the hospital information technology team to carry out a security audit of the hospital management information system (HMIS). In this study, we measured the quality of the HMIS software in X Hospital using ISO 25023 which focused on the security aspects of the outpatient service module and drug services in the outpatient pharmacy unit. The security aspect based on the ISO 25023 standard consists of five main characteristics, namely: confidentiality, integrity, non-repudiation, accountability and authenticity. In the early stages, calculations are carried out to find the value of each measurement standard which is denoted by (X). The X value is based on the standard calculation range of values 0 and 1. The threshold value is determined at 0.80 to categorize the point quality whether it is not good or has met the ISO 25023 quality point. The results of software quality measurements show Internal Data Corruption Prevention is worth 0.75 and is at below a predetermined threshold value. Based on these results, it is recommended to improve one of them by replication the database to minimize the possibility of Internal Data Corruption Prevention. In this study, all aspects of software quality have an average value above the threshold, so it can be concluded that HMIS in RS X meets ISO 25023 standards.
Analisis Komparasi Algoritma DBSCAN dan K-Means dalam Pemetaan Segmentasi Pasien Rawat Inap Menggunakan Model RFMT: Comparative Analysis of DBSCAN and K-Means Algorithms in Inpatient Patient Segmentation Using the RFMT Model Approach Shodikin, Mohamad; Taniasari, Nungky
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 4 (2025): MALCOM October 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i4.2218

Abstract

Rumah sakit memainkan peran strategis dalam memberikan layanan kesehatan berkualitas, khususnya layanan rawat inap. Model segmentasi pasien yang akurat diperlukan untuk mendukung pengambilan keputusan strategis dan meningkatkan loyalitas pasien. Studi ini mengusulkan analisis komparatif dua algoritma clustering, DBSCAN dan K-Means, untuk segmentasi rawat inap menggunakan model Recency, Frequency, Monetary, and Interpurchase Time (RFMT). Berbeda dengan studi sebelumnya yang berfokus pada ritel dan pemasaran, penelitian ini menerapkan RFMT secara spesifik pada data rawat inap rumah sakit. Dataset yang diperoleh dari Rumah Sakit X di Sidoarjo periode (Januari–Oktober 2022), telah diproses terlebih dahulu dan diubah ke dalam format RFMT. Algoritma DBSCAN dan K-Means dievaluasi menggunakan indeks validitas klaster internal: Silhouette Index (SI) dan Calinski-Harabasz Index (CHI). Hasil eksperimen menunjukkan bahwa DBSCAN mencapai SI terbaiknya sebesar 0,384 (Eps=0,9, MinPts=24) tetapi menghasilkan banyak titik noise, sementara K-Means berkinerja lebih baik dengan SI=0,399 dan CHI=14.625,319 pada k=7 klaster. Temuan ini menyoroti bahwa K-Means menghasilkan klaster yang lebih stabil dan valid dalam konteks ini, sementara DBSCAN mengalami kesulitan karena distribusi kepadatan dataset. Studi ini berkontribusi dengan menunjukkan penerapan clustering berbasis RFMT pada data rumah sakit dan membandingkan kekuatan dan keterbatasan dua algoritma yang banyak digunakan.
Co-Authors Cahyono, Warna Agung Kriswibowo, Rony Meisyarani, Jihan Salsabila Putri Ariatna Alia Sani, Rosi Rijal Taniasari, Nungky